Real-time 3D visual sensor for robust object recognition

Attamimi, Muhammad; Mizutani, Akira; Nagaoka, Tomoaki; Nagai, Takayuki; Funakoshi, Kotaro; Nakano, Mikio

doi:10.1109/iros.2010.5650455

Cited by 25 publications

(25 citation statements)

References 8 publications

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“…Figure 1 shows the block diagram of the proposed method. Here given an input image color and the Time of Flight (TOF) data, is segmented using SLIC super pixel [16,17]. Next several key points are extracted and labeled as a feature, for matching, using SURF [18].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…A 3D visual sensor [16] which consists of a TOF and two CCD cameras is used to capture color and 3D information to construct a database. To obtain the visual information, a small handheld observation table with an XBee wireless controller is installed on a robot that enables the observation of the object from various viewpoints.…”

Section: Databasementioning

confidence: 99%

“…The proposed object detection method uses visual sensor, shown in Figure 2, which acquires color information in real time by calibrating the TOF (Time of Flight) and two CCD cameras [16].…”

Section: Input Imagementioning

confidence: 99%

See 2 more Smart Citations

Object Detection Using SURF and Superpixels

Lopez-de-la-Calleja¹,

Nagai²,

Attamimi³

et al. 2013

JSEA

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This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%

See 1 more Smart Citation

Object Detection Using SURF and Superpixels

Lopez-de-la-Calleja¹,

Nagai²,

Attamimi³

et al. 2013

JSEA

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“…Generally, it is difficult to recognize objects that have the same color and/or with no textures. For future work, we plan to use an object recognition method that integrates 3D shape information (31), which can significantly improve object recognition performance.…”

Section: Image Processingmentioning

confidence: 99%

Learning Novel Objects for Domestic Service Robots

Attamimi¹,

Nagaoka²,

Nagai³

et al. 2012

The Future of Humanoid Robots - Research and Applications

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“…The robot can acquire visual information form the 3D visual sensor [12], auditory information by shaking the object, and haptic information by grasping it. We also propose an online algorithm of multimodal categorization based on the autonomously acquired multimodal information and words, which are partially given by the human user.…”

Section: Introductionmentioning

confidence: 99%

Autonomous acquisition of multimodal information for online object concept formation by robots

Araki

Nagaoka

Nagai

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper proposes a robot that acquires multimodal information, i.e. auditory, visual, and haptic information, fully autonomous way using its embodiment. We also propose an online algorithm of multimodal categorization based on the acquired multimodal information and words, which are partially given by human users. The proposed framework makes it possible for the robot to learn object concepts naturally in everyday operation in conjunction with a small amount of linguistic information from human users. In order to obtain multimodal information, the robot detects an object on a fla surface. Then the robot grasps and shakes it for gaining haptic and auditory information. For obtaining visual information, the robot uses a hand held small observation table, so that the robot can control the viewpoints for observing the object. As for the multimodal concept formation, the multimodal LDA using Gibbs sampling is extended to the online version in this paper. The proposed algorithms are implemented on a real robot and tested using real everyday objects in order to show validity of the proposed system.

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Real-time 3D visual sensor for robust object recognition

Cited by 25 publications

References 8 publications

Object Detection Using SURF and Superpixels

Object Detection Using SURF and Superpixels

Learning Novel Objects for Domestic Service Robots

Autonomous acquisition of multimodal information for online object concept formation by robots

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